We report the top-1 accuracy of the supervised pre-trained model on ImageNet-1k in the original paper [1]}. For fine-tuning of MIM pre-trained model on ImageNet-1k, we follow the setting of SimMIM [2]} and use the AdamW optimizer with a weight decay of \(5e\) -2, a base learning rate of \(5e\) -3 with a layer decay of \(0.8\) . The learning rate is scheduled via cosine strategy and we use 20 epochs for warm-up and train for 100 epochs in total. The batch size is 2048. We adopt the DropPath of \(0.1\)  and gradient clipping. The data augmentations contain AutoAug [3]}, Mixup [4]}, CutMix [5]}, color jitter, random erasing [6]}, and label smoothing [7]}.
The settings of the pose estimation are the same as the details in Section .
